…the study and practice of making maps. Combining science, aesthetics, and technique, cartography builds on the premise that reality can be modeled in ways that communicate spatial information effectively.

So let’s flex some cartographic muscle and avoid cartostrophe

Here are a sample:

• Physical maps show landscape features like rivers and land masses
• Topographic maps show the contours of elevation lines
• Climate maps can show atmospheric conditions
• Thematic maps show special topics like election results or even energy usage in areas.

• Title : Should clearly introduce the topic of the map
• Legend : Gives an understanding of the range, and variation in data (make the units easy to read)
• North Arrow : Offers orientation for cardinal directions
• Scale Bar : Shows the scale whether it is large or small scale map and amount of detail to be excepted.¹
• Sources : Give credit where credit is deserved
• Date : It’s a good practice to date your maps so you remember when it was made
• Name : Also don’t forget to take credit for your work

• tm_polygons Create a polygon layer (with borders)
• tm_symbols Create a layer of symbols
• tm_lines Create a layer of lines
• tm_raster Create a raster layer
• tm_text Create a layer of text labels
• tm_basemap Create a layer of basemap tiles
• tm_tiles Create a layer of overlay tiles

• tm_fill Create a polygon layer (without borders)
• tm_borders Create polygon borders
• tm_bubbles Create a layer of bubbles
• tm_squares Create a layer of squares
• tm_dots Create a layer of dots
• tm_markers Create a layer of markers
• tm_iso Create a layer of iso/contour lines
• tm_rgb Create a raster layer of an image

# create borders
tm_shape(nycbb) +
  tm_borders() 

# create the fill
tm_shape(nycbb) +
  tm_fill() 

# combine them
tm_shape(nycbb) +
  tm_fill() +
  tm_borders() 

## tmap example

# or polygons
tm_shape(nycbb) + 
    tm_polygons()

map1 <- tm_shape(nycbb) + tm_fill(col = "red")
map2 <- tm_shape(nycbb) + tm_fill(col = "red", alpha  = .2)
map3 <- tm_shape(nycbb) + tm_borders(col = "grey")
map4 <- tm_shape(nycbb) + tm_borders(col = "blue", lwd = 0.5, lty = 2)
tmap_arrange(map1, map2, map3, map4)

The common classification methods are:

• Equal Interval will deal with the range it will cut the range into equal sizes
• Quantile will deal with the population it will cut the population into equal sizes
• Natural Breaks will algorithmically minimize within-class variance and maximize between-class differences.
• Manual allows full control to the user
• Geometric Interval is created using geometric progression, a sequence of numbers where each term after the first is found by multiplying the previous one by a fixed, non-zero number called the common ratio

• tm_layout Adjust the layout (main function)
• tm_legend Adjust the legend
• tm_view Configure the interactive view mode
• tm_style Apply a predefined style
• tm_format Apply a predefined format

• tm_grid Create grid lines
• tm_scale_bar Create a scale bar
• tm_compass Create a map compass
• tm_credits Create a text for credits
• tm_logo Create a logo
• tm_xlab and tm_ylab Create axis labels
• tm_minimap Create a minimap (view mode only)

Make three maps from your usCounties comparing the percent difference in areas.

Make sure to have a north arrow, scale bar, source, legend, and title.

You can add a projection on the fly by specifying the projection attribute in tm_shape too if you want to play with that.

subway <- read_sf( "../Shapes/SubwayLines") %>% 
    st_transform(2263) %>% mutate(rt_symbol = as.factor(rt_symbol))

subwayPalette <- c("1"='#EE352E', "4"='#00933C', "7"='#B933AD',
                   "A"='#0039A6',"B"='#FF6319', "G"= '#6CBE45', 
                   "J"='#996633',"L"='#A7A9AC',"N"='#FCCC0A')

tm_shape(nycbb) + tm_borders(col = "grey") +
      tm_shape(subway) + 
          tm_lines(col = 'rt_symbol', palette = subwayPalette) +
      tm_facets(by = "rt_symbol", free.coords = FALSE, nrow = 3)

you might need image magick

we will use the tmap_animation function to create a gif.

The parameters that are important are: filename is the filename of the video (should be a .gif or .mpg file] delay is the time between images (in 1/100th of a second) so 4 seconds is 400. loop is a logical that determines whether the animation is looped, or an integer value that determines how many times the animation is looped.

subwayMaps <- tm_shape(nycbb, simplify = 0.8) + 
    tm_borders(col = "grey") +
      tm_shape(subway) + 
          tm_lines(col = 'rt_symbol', palette = subwayPalette) +
      tm_facets(along = "rt_symbol", free.coords = FALSE)

tmap_animation(subwayMaps, filename = "subway.gif",
               delay = 150, loop = TRUE)

Leaflet is a great interactive mapping platform and couldn’t be simpler to add to your rmarkdown document/slides or shiny dashboards. It can deal with sf and sp objects so you don’t have to make any conversions with your spatial data!

we can set the view and add a provider tile

library(leaflet)
m <- leaflet() %>% 
    setView(lng = -73.9691305, lat = 40.7764627, zoom = 11) %>% 
    addProviderTiles(providers$CartoDB.Positron)
m

m %>% addPolygons(data = nycbb %>% st_transform(4326)) %>% 
    addPolylines(data = subway %>% st_transform(4326))

pal <- colorFactor(palette = c("#EE352E", "#00933C", "#B933AD",
                           "#0039A6", "#FF6319", "#6CBE45",
                           "#996633", "#A7A9AC", "#FCCC0A"), 
               levels = c("1", "4", "7", "A", "B",
                          "G", "J", "L", "N"))

m %>% addPolygons(data = nycbb %>% st_transform(4326),fillColor ="NA") %>% 
    addPolylines(data = subway %>% st_transform(4326),
                 color = ~pal(rt_symbol))

Add usCounties to a leaflet map and create a color palette for colorNumeric using one of your percent differences.